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2000
Volume 22, Issue 7
  • ISSN: 1570-1794
  • E-ISSN: 1875-6271

Abstract

Background

L-type amino acid transporter-1 is a drug that stimulates the functions of the brain’s central nervous system. Membrane transporters have evolved, leading to a distinct approach in L-type amino acid transporter-1 drug delivery. One of the transporters used for transporting drugs across biological membranes is the L-type amino acid transporter-1. It is widely discussed in the medicinal field.

Objectives

Numerous investigations indicate a close connection between the properties of alkanes and the diversity of central nervous system drugs in the brain, specifically log P and molecular weight. One important study that analyzes structural properties is focused on topological descriptors. Recently, topological indices have found application in the development of quantitative structure-activity relationships. These indices are correlated with the physicochemical properties of BCNS-acting drugs and their biological activity.

Methods

The study employs significant methods of calculating topological indices: the edge set partition method and the Djokovi´c-Winkler relation (cut method) are utilized to calculate the values of these descriptors.

Results

The results of distance and degree-based topological descriptors have been derived. The strong correlation between topological descriptors and the physicochemical properties of BCNS-acting drugs has been studied.

Conclusion

This article identifies important topological features for various CNS medications, aiming to support researchers in understanding the properties of molecules and their biological activity. Furthermore, we demonstrate how strongly these behaviors correspond to the physicochemical properties of central nervous system drugs.

© 2025 Bentham Science Publishers
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2025-12-10

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References

  1. KimS.M. FaixP.H. SchnitzerJ.E. Overcoming key biological barriers to cancer drug delivery and efficacy.J. Control. Release2017267153010.1016/j.jconrel.2017.09.016 28917530
     [Google Scholar]
  2. MillerG. Drug targeting. Breaking down barriers.Science200229755841116111810.1126/science.297.5584.1116 12183610
     [Google Scholar]
  3. LeeJ.T. RochellS.J. KriseldiR. KimW.K. MitchellR.D. Functional properties of amino acids: Improve health status and sustainability.Poult. Sci.2023102110228810.1016/j.psj.2022.102288 36436367
     [Google Scholar]
  4. SuteraF.M. De CaroV. GiannolaL.I. Small endogenous molecules as moiety to improve targeting of CNS drugs.Expert Opin. Drug Deliv.20171419310710.1080/17425247.2016.1208651 27367188
     [Google Scholar]
  5. ZhangY. ChengQ. XueY. YaoK. SyedaM.Z. XuJ. WuJ. WangZ. TangL. MuQ. LAT1 targeted brain delivery of temozolomide and sorafenib for effective glioma therapy.Nano Res.20231679743975110.1007/s12274‑023‑5568‑3
     [Google Scholar]
  6. KageyamaT. NakamuraM. MatsuoA. YamasakiY. TakakuraY. HashidaM. KanaiY. NaitoM. TsuruoT. MinatoN. ShimohamaS. The 4F2hc/LAT1 complex transports l-DOPA across the blood-brain barrier.Brain Res.20008791-211512110.1016/S0006‑8993(00)02758‑X 11011012
     [Google Scholar]
  7. DickensD. WebbS.D. AntonyukS. GiannoudisA. OwenA. RädischS. HasnainS.S. PirmohamedM. Transport of gabapentin by LAT1 (SLC7A5).Biochem. Pharmacol.201385111672168310.1016/j.bcp.2013.03.022 23567998
     [Google Scholar]
  8. van BreeJ.B.M.M. AudusK.L. BorchardtR.T. Carrier-mediated transport of baclofen across monolayers of bovine brain endothelial cells in primary culture.Pharm. Res.19885636937110.1023/A:1015959628008 3244648
     [Google Scholar]
  9. GyntherM. JalkanenA. LehtonenM. ForsbergM. LaineK. RopponenJ. LeppänenJ. KnuutiJ. RautioJ. Brain uptake of ketoprofen-lysine prodrug in rats.Int. J. Pharm.20103991-212112810.1016/j.ijpharm.2010.08.019 20727958
     [Google Scholar]
  10. GyntherM. LaineK. RopponenJ. LeppänenJ. MannilaA. NevalainenT. SavolainenJ. JärvinenT. RautioJ. Large neutral amino acid transporter enables brain drug delivery via prodrugs.J. Med. Chem.200851493293610.1021/jm701175d 18217702
     [Google Scholar]
  11. KillianD.M. HermelingS. ChikhaleP.J. Targeting the cerebrovascular large neutral amino acid transporter (LAT1) isoform using a novel disulfide-based brain drug delivery system.Drug Deliv.2007141253110.1080/10717540600559510 17107928
     [Google Scholar]
  12. PeuraL. MalmiojaK. HuttunenK. LeppänenJ. HämäläinenM. ForsbergM.M. RautioJ. LaineK. LaineK. Design, synthesis and brain uptake of LAT1-targeted amino acid prodrugs of dopamine.Pharm. Res.201330102523253710.1007/s11095‑012‑0966‑3 24137801
     [Google Scholar]
  13. PeuraL. MalmiojaK. LaineK. LeppänenJ. GyntherM. IsotaloA. RautioJ. Large amino acid transporter 1 (LAT1) prodrugs of valproic acid: new prodrug design ideas for central nervous system delivery.Mol. Pharm.2011851857186610.1021/mp2001878 21770378
     [Google Scholar]
  14. RaufA. NaeemM. HanifA. Quantitative structure-properties relationship analysis of EIGEN‐VALUE ‐based indices using COVID ‐19 drugs structure.Int. J. Quantum Chem.20231234e2703010.1002/qua.27030 36718482
     [Google Scholar]
  15. AslamA. BashirY. AhmadS. GaoW. On topological indices of certain dendrimer structures.Zeitschrift fur Naturforsch. Sect A J Phys Sci.201772655966
     [Google Scholar]
  16. HosamaniS. PerigidadD. JamagoudS. MaledY. GavadeS. QSPR anlysis of certain degree based topological indices.J. Stat. Appl. Probab.20176236137110.18576/jsap/060211
     [Google Scholar]
  17. Randi’cM. Comparative structure-property studies: Regressions using a single descriptor.Croat. Chem. Acta193366289312
     [Google Scholar]
  18. Randi’cM. Quantitative Structure-Propert Relationship: boiling points and planar benzenoids.New J. Chem.19962010011009
     [Google Scholar]
  19. ShanmukhaM.C. BasavarajappaN.S. AnilkumarK.N. Predicting physico-chemical properties of octane isomers using QSPR Approach, Malaya.J. Math.202081104116
     [Google Scholar]
  20. HayatS. ImranM. LiuJ.B. Correlation between the Estrada index and Q-electronic energies for benzenoid hydrocarbons with applica-tions to boron nanotubes.Int. J. Quant. Chem2019
     [Google Scholar]
  21. AslamA. AhmadS. GaoW. On topological indices of boron triangular nanotubes.Z. Naturforsch. A201772871171610.1515/zna‑2017‑0135
     [Google Scholar]
  22. HayatS. WangS. LiuJ.B. Valency-based topological descriptors of chemical networks and their applications.Appl. Math. Model.20186016417810.1016/j.apm.2018.03.016
     [Google Scholar]
  23. ToganM. YurttasI.N. Cangul. Some formulae and inequalities on several Zagreb indices of r-subdivision graphs.Enlightments of Pure and Applied Mathematics2015112945
     [Google Scholar]
  24. HosamaniS.M. LokeshaV. CangulI.N. DevendraiahK.M. On certain topological indices of the derived graphs of subdivision graphs.TWMS J. Appl. & Eng. Math.201662324332
     [Google Scholar]
  25. AlwardiA. AlqesmahA. RangarajanR. CangulI.N. Entire Zagreb indices of graphs.Discrete Math. Algorithms Appl.2018103185003710.1142/S1793830918500374
     [Google Scholar]
  26. HayatS. ImranM. LiuJ.B. An efficient computational technique for degree and distance based topological descriptors with applications.IEEE Access201973227632296
     [Google Scholar]
  27. HayatS. Distance-based graphical indices for predicting thermodynamic properties of benzenoid hydrocarbons with applications.Comput. Mater. Sci.202323011249210.1016/j.commatsci.2023.112492
     [Google Scholar]
  28. GutmanI. Degree based topological indices.Croat. Chem. Acta201386435136110.5562/cca2294
     [Google Scholar]
  29. FurtulaB. GraovacA. VukičevićD. Augmented Zagreb index.J. Math. Chem.201048237038010.1007/s10910‑010‑9677‑3
     [Google Scholar]
  30. ShirdelG.H. RezaPour, H.; Sayadi, A.M; The hyper-zagreb index of graph operations.Iran. J. Math. Chem.201342213220
     [Google Scholar]
  31. HasniR. Md HusinN.H. DuZ. Unicyclic graphs with maximum Randić indices.Commun. Comb. Optim.202381161172
     [Google Scholar]
  32. TimmanaikarS. Polynomial Regression Analysis for Some Anticancer Drugs with Degree Based Topological Indices.Tuijin Jishu/Journal of Propulsion Technology2024451
     [Google Scholar]
  33. ZhouH. AhmadM. SiddiquiM.K. On some bounds of first Gourava index for Ψ-sum graphs.Discrete Math. Algorithms Appl.20242024245000610.1142/S179383092450006X
     [Google Scholar]
  34. WienerH. Structural determination of paraffin boiling points.J. Am. Chem. Soc.1947691172010.1021/ja01193a005 20291038
     [Google Scholar]
  35. Sahaya VijayJ. RoyS. Computation of Wiener Descriptor for Melamine Cyanuric Acid Structure.Polycycl. Aromat. Compd.20244421057107110.1080/10406638.2023.2186441
     [Google Scholar]
  36. GutmanI. A formula for the Wiener number of trees and its extension to graphs containing cycles.Graph Theory Notes NY.199427915
     [Google Scholar]
  37. GutmanI. AshrafiA.R. The edge version of the Szeged index.Croat. Chem. Acta200881263266
     [Google Scholar]
  38. KhalifehM.H. Yousefi-AzariH. AshrafiA.R. The first and second Zagreb indices of some graph operations.Discrete Appl. Math.2009157480481110.1016/j.dam.2008.06.015
     [Google Scholar]
  39. KhadikarP.V. KarmarkarS. AgrawalV.K. A novel PI index and its applications to QSPR/QSAR studies.J. Chem. Inf. Comput. Sci.200141493494910.1021/ci0003092 11500110
     [Google Scholar]
  40. TratnikN. Computing the Mostar index in networks with applications to molecular graphs.arXiv:1904.041312019, 2019
     [Google Scholar]
  41. PurisE. GyntherM. AuriolaS. HuttunenK.M. L-Type amino acid transporter 1 as a target for drug delivery.Pharm. Res.20203758810.1007/s11095‑020‑02826‑8 32377929
     [Google Scholar]
  42. HayatS. AlanaziS.J.F. LiuJ-B. Two novel temperature-based topological indices with strong potential to predict physicochemical properties of polycyclic aromatic hydrocarbons with applications to silicon carbide nanotubes.Phys. Scr.202499505502710.1088/1402‑4896/ad3ada
     [Google Scholar]
  43. HayatS. MahadiH. AlanaziS.J.F. WangS. Predictive potential of eigenvalues-based graphical indices for determining thermodynamic properties of polycyclic aromatic hydrocarbons with applications to polyacenes.Comput. Mater. Sci.202423811294410.1016/j.commatsci.2024.112944
     [Google Scholar]
/content/journals/cos/10.2174/0115701794349717241024100059
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Quantitative Structure-Property Relationship (QSPR) Modeling of Central Nervous System (CNS) Drug Activity using Molecular Descriptors
Curr. Org. Synth. 22, 799 (2025); https://doi.org/10.2174/0115701794349717241024100059
/content/journals/cos/10.2174/0115701794349717241024100059
/content/journals/cos/10.2174/0115701794349717241024100059
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  • Article Type:     Research Article
Keyword(s): central nervous system acting drugs; djokovi´c-winkler; L-type amino acid transporter-1; linear regression models; QSPR analysis; topological descriptors

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